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Using Twitter to Measure Public Discussion of Diseases: A Case Study.

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Twitter data can estimate disease prevalence, but biases in demographics and language ambiguity affect accuracy. Correcting these biases significantly improves correlation, making Twitter a more reliable tool for public health research.

Keywords:
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Area of Science:

  • Computational epidemiology
  • Social media analytics
  • Public health surveillance

Background:

  • Twitter is increasingly utilized for estimating disease prevalence.
  • Measurements can be biased due to sampling and natural language ambiguity.

Purpose of the Study:

  • Characterize biases in Twitter-based disease prevalence estimation.
  • Assess how biases vary across different diseases.

Main Methods:

  • Correlated self-reported disease prevalence with Twitter mentions for 22 diseases.
  • Identified and corrected for demographic variance and natural language ambiguity in Twitter data.
  • Quantified disease over/underrepresentation on Twitter relative to prevalence.

Main Results:

  • Correcting for demographics more than doubled the correlation between Twitter mentions and prevalence (0.113 to 0.258).
  • Ambiguity correction improved correlation to 0.208; combined corrections yielded a correlation of 0.366.
  • Cancer was most overrepresented, while high cholesterol was most underrepresented on Twitter compared to prevalence.

Conclusions:

  • Twitter can gauge public interest and concern regarding diseases.
  • Adjusting for population demographics and word ambiguity enhances Twitter data's utility for comparative disease analysis.